This is the replication notebook for Lab-08 from the course SOC 4650/5650: Introduction to GISc.
The following code loads the package dependencies for our analysis:
# tidyverse packages
library(dplyr) # data wrangling
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
# spatial packages
library(mapview) # preview spatial data
library(sf) # spatial data tools
library(tidycensus) # data wrangling
library(tigris) # data wrangling
# other packages
library(here) # file path tools
First, we’ll download and preview the variables using the load_variables() function from tidycensus.
acs <- load_variables(2018, "acs5", cache = TRUE)
The variable represents “PUBLIC ASSISTANCE INCOME OR FOOD STAMPS/SNAP IN THE PAST 12 MONTHS FOR HOUSEHOLDS”.
First, we’ll download the relevant ACS data using get_acs(). We get the data for all counties by specifying "county" as the geography:
snapCounties <- get_acs(geography = "county", year = 2018, state = 29, variable = "B19058_002", survey = "acs5")
Getting data from the 2014-2018 5-year ACS
Now we’ll download geometric data to go along with our tabular data. We’ll start by downloading the county boundary data and converting it to an sf object. The video shows you how to do this process in two steps:
counties <- counties(state = 29, cb = FALSE)
counties <- st_as_sf(counties)
However, there is now an option within tigris to do it in a single call:
counties <- counties(state = 29, cb = FALSE, class = "sf")
This makes it just a bit quicker to get mappable results. We can preview our shapefile with mapview:
mapview(counties)
Next, we’ll clean and merge our data. First, we’ll clean our county geometry data, retaining only the GEOID column:
counties <- select(counties, GEOID)
Then we’ll tidy up the demographic data:
snapCounties %>%
rename(
snap = estimate,
snap_moe = moe
) %>%
select(-variable) -> snapCounties
Finally, we’ll combine our data:
snapCounties <- left_join(counties, snapCounties, by = "GEOID")
Again, we’ll preview our data with mapview:
mapview(snapCounties, zcol = "snap")
The zcol argument allows us to map the values of a particular variable as part of our preview.
Finally, we’ll write these data as a .shp file:
st_write(snapCounties, dsn = here("data", "MO_SNAP_Households", "MO_SNAP_Households.shp"), delete_dsn = TRUE)
First, we’ll download the relevant ACS data using get_acs(). We get the data for all tracts by specifying "tract" as the geography and specifying 189 for the county argument:
snapTracts <- get_acs(geography = "tract", year = 2018, state = 29, county = 189, variable = "B19058_002", survey = "acs5")
Getting data from the 2014-2018 5-year ACS
Now we’ll download geometric data to go along with our tabular data:
tracts <- tracts(state = 29, county = 189, cb = FALSE, class = "sf")
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We can preview our shapefile with mapview:
mapview(tracts)
Next, we’ll clean and merge our data. First, we’ll clean our county geometry data, retaining only the GEOID column:
tracts <- select(tracts, GEOID)
Then we’ll tidy up the demographic data:
snapTracts %>%
rename(
snap = estimate,
snap_moe = moe
) %>%
select(-variable) -> snapTracts
Finally, we’ll combine our data:
snapTracts <- left_join(tracts, snapTracts, by = "GEOID")
Again, we’ll preview our data with mapview:
mapview(snapTracts, zcol = "snap")
Finally, we’ll write these data as a .shp file:
st_write(snapTracts, dsn = here("data", "STL_SNAP_Households", "STL_SNAP_Households.shp"), delete_dsn = TRUE)